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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
added preview option
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AUTOMATIC
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db6db585eb
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fd66199769
@ -176,6 +176,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)
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if state.interrupted:
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# if we are interruped, sample returns just noise
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# use the image collected previously in sampler loop
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samples_ddim = shared.state.current_latent
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x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -42,6 +42,8 @@ def p_sample_ddim_hook(sampler_wrapper, x_dec, cond, ts, *args, **kwargs):
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img_orig = sampler_wrapper.sampler.model.q_sample(sampler_wrapper.init_latent, ts)
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x_dec = img_orig * sampler_wrapper.mask + sampler_wrapper.nmask * x_dec
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state.current_latent = x_dec
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return sampler_wrapper.orig_p_sample_ddim(x_dec, cond, ts, *args, **kwargs)
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@ -141,6 +143,9 @@ class KDiffusionSampler:
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self.func = getattr(k_diffusion.sampling, self.funcname)
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self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
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def callback_state(self, d):
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state.current_latent = d["denoised"]
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
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t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
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sigmas = self.model_wrap.get_sigmas(p.steps)
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@ -157,7 +162,7 @@ class KDiffusionSampler:
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
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return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
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def sample(self, p, x, conditioning, unconditional_conditioning):
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sigmas = self.model_wrap.get_sigmas(p.steps)
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@ -166,6 +171,6 @@ class KDiffusionSampler:
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
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samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False)
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samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
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return samples_ddim
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@ -39,6 +39,7 @@ gpu = torch.device("cuda")
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device = gpu if torch.cuda.is_available() else cpu
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batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
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class State:
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interrupted = False
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job = ""
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@ -46,6 +47,8 @@ class State:
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job_count = 0
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sampling_step = 0
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sampling_steps = 0
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current_latent = None
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current_image = None
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def interrupt(self):
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self.interrupted = True
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@ -99,6 +102,7 @@ class Options:
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"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
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"upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
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"show_progressbar": OptionInfo(True, "Show progressbar"),
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"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
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}
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def __init__(self):
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@ -9,6 +9,8 @@ import sys
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import time
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import traceback
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import numpy as np
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import torch
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from PIL import Image
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import gradio as gr
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@ -119,6 +121,9 @@ def wrap_gradio_call(func):
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print("Arguments:", args, kwargs, file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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shared.state.job = ""
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shared.state.job_count = 0
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res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
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elapsed = time.perf_counter() - t
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@ -134,11 +139,9 @@ def wrap_gradio_call(func):
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def check_progress_call():
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if not opts.show_progressbar:
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return ""
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if shared.state.job_count == 0:
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return ""
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return "", gr_show(False), gr_show(False)
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progress = 0
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@ -149,9 +152,29 @@ def check_progress_call():
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progress = min(progress, 1)
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progressbar = ""
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if opts.show_progressbar:
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progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
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return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>"
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image = gr_show(False)
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preview_visibility = gr_show(False)
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if opts.show_progress_every_n_steps > 0:
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if (shared.state.sampling_step-1) % opts.show_progress_every_n_steps == 0 and shared.state.current_latent is not None:
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x_sample = shared.sd_model.decode_first_stage(shared.state.current_latent[0:1].type(shared.sd_model.dtype))[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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shared.state.current_image = Image.fromarray(x_sample)
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image = shared.state.current_image
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if image is None or progress >= 1:
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image = gr.update(value=None)
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else:
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preview_visibility = gr_show(True)
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return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
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def roll_artist(prompt):
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@ -204,6 +227,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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with gr.Column(variant='panel'):
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with gr.Group():
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txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
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txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery')
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@ -251,8 +275,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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check_progress.click(
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fn=check_progress_call,
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show_progress=False,
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inputs=[],
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outputs=[progressbar],
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outputs=[progressbar, txt2img_preview, txt2img_preview],
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)
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@ -337,13 +362,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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with gr.Column(variant='panel'):
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with gr.Group():
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img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
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img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery')
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with gr.Group():
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with gr.Row():
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interrupt = gr.Button('Interrupt')
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save = gr.Button('Save')
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img2img_send_to_img2img = gr.Button('Send to img2img')
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img2img_send_to_inpaint = gr.Button('Send to inpaint')
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img2img_send_to_extras = gr.Button('Send to extras')
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interrupt = gr.Button('Interrupt')
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progressbar = gr.HTML(elem_id="progressbar")
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@ -426,8 +454,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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check_progress.click(
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fn=check_progress_call,
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show_progress=False,
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inputs=[],
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outputs=[progressbar],
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outputs=[progressbar, img2img_preview, img2img_preview],
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)
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interrupt.click(
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@ -463,6 +492,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
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outputs=[init_img_with_mask],
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)
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img2img_send_to_img2img.click(
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fn=lambda x: image_from_url_text(x),
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_js="extract_image_from_gallery",
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inputs=[img2img_gallery],
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outputs=[init_img],
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)
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img2img_send_to_inpaint.click(
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fn=lambda x: image_from_url_text(x),
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_js="extract_image_from_gallery",
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inputs=[img2img_gallery],
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outputs=[init_img_with_mask],
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)
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with gr.Blocks(analytics_enabled=False) as extras_interface:
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with gr.Row().style(equal_height=False):
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with gr.Column(variant='panel'):
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17
script.js
17
script.js
@ -79,6 +79,23 @@ function addTitles(root){
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global_progressbar = progressbar
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var mutationObserver = new MutationObserver(function(m){
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txt2img_preview = gradioApp().getElementById('txt2img_preview')
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txt2img_gallery = gradioApp().getElementById('txt2img_gallery')
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img2img_preview = gradioApp().getElementById('img2img_preview')
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img2img_gallery = gradioApp().getElementById('img2img_gallery')
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if(txt2img_preview != null && txt2img_gallery != null){
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txt2img_preview.style.width = txt2img_gallery.clientWidth + "px"
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txt2img_preview.style.height = txt2img_gallery.clientHeight + "px"
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}
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if(img2img_preview != null && img2img_gallery != null){
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img2img_preview.style.width = img2img_gallery.clientWidth + "px"
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img2img_preview.style.height = img2img_gallery.clientHeight + "px"
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}
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window.setTimeout(requestProgress, 500)
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});
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mutationObserver.observe( progressbar, { childList:true, subtree:true })
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15
style.css
15
style.css
@ -31,6 +31,20 @@ button{
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max-width: 10em;
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}
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#txt2img_preview, #img2img_preview{
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position: absolute;
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width: 320px;
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left: 0;
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right: 0;
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margin-left: auto;
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margin-right: auto;
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z-index: 100;
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}
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#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
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display: none;
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}
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fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
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position: absolute;
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top: -0.6em;
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@ -96,3 +110,4 @@ input[type="range"]{
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text-align: right;
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border-radius: 8px;
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}
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5
webui.py
5
webui.py
@ -125,7 +125,8 @@ def wrap_gradio_gpu_call(func):
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shared.state.sampling_step = 0
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shared.state.job_count = -1
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shared.state.job_no = 0
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shared.state.current_latent = None
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shared.state.current_image = None
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with queue_lock:
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res = func(*args, **kwargs)
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@ -163,7 +164,7 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
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if __name__ == "__main__":
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# make the program just exit at ctrl+c without waiting for anything
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def sigint_handler(sig, frame):
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print(f'Interrupted with singal {sig} in {frame}')
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print(f'Interrupted with signal {sig} in {frame}')
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os._exit(0)
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